Data-centric science for materials innovation. With the development and availability of high-speed computers, networks, and huge data storage, researchers can utilize a large volume and wide variety of materials data generated by experimental facilities and computations. The emergence of these big data as well as advanced analytical techniques has opened unprecedented opportunities for materials research. The cover shows miscibility maps for various combinations of elements in binary alloys. The maps are constructed using data derived from experimental thermodynamic databases, electronic-structure databases, along with data predicted by harnessing machine-learning methods on existing repositories of electronic-structure data. Red and green symbols indicate immiscible and miscible systems, respectively. The white symbols represent systems without available experimental data, while the blue symbols indicate the boundary of alloys consisting of dissimilar elements. The background shows 5000 predicted thermal conductivity results. See the technical theme that begins on p. 659.